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The "Hive/LanguageManual/DDL/BucketedTables" page has been changed by PaulYang.
http://wiki.apache.org/hadoop/Hive/LanguageManual/DDL/BucketedTables?action=diff&rev1=7&rev2=8
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First there’s table creation:
{{{
- CREATE TABLE user_info_bucketed(userid BIGINT, firstname STRING, lastname STRING)
+ CREATE TABLE user_info_bucketed(user_id BIGINT, firstname STRING, lastname STRING)
COMMENT 'A bucketed copy of user_info'
PARTITIONED BY(ds STRING)
- CLUSTERED BY(userid) INTO 256 BUCKETS;
+ CLUSTERED BY(user_id) INTO 256 BUCKETS;
}}}
- Then we populate this, making sure to use 256 reducers:
+ Note that we specify a column (user_id) to base the bucketing.
+
+ Then we populate the table
{{{
+ set hive.enforce.bucketing = true;
+ FROM user_id
- set mapred.reduce.tasks = 256;
- FROM (
- FROM user_info u
- SELECT CAST(userid AS BIGINT) % 256 AS bucket_id, userid, firstname, lastname
- WHERE d.ds='2009-02-25'
- CLUSTER BY bucket_id
- ) c
INSERT OVERWRITE TABLE user_info_bucketed
PARTITION (ds='2009-02-25')
- SELECT userid, firstname, lastname;
+ SELECT userid, firstname, lastname WHERE ds='2009-02-25';
}}}
- Note that I’m clustering by the integer version of userid. This might otherwise cluster
by userid as a STRING (depending on the type of userid in user_info), which uses a totally
different hash. It's important for the hashing function to be of the correct data type, since
otherwise we'll expect userids in bucket 1 to satisfy (big_hash(userid) mod 256 == 0), but
instead we'll be getting (string_hash(userid) mod 256 == 0). It's also good form to have
all of your tables use the same type (eg, BIGINT instead of STRING) since that way your sampling
from multiple tables will give you the same userids, letting join efficiently sample and join.
+ The command {{{set hive.enforce.bucketing = true; }}} allows the correct number of reducers
and the cluster by column to be automatically selected based on the table. Otherwise, you
would need to set the number of reducers to be the same as the number of buckets a la {{{set
mapred.reduce.tasks = 256;}}} and have {{{CLUSTER BY ...}}} clause in the select.
+ How does Hive distribute the rows across the buckets? In general, the bucket number is determined
by the expression {{{hash_function(bucketing_column) mod num_buckets}}}. (There's a '0x7FFFFFFF
in there too, but that's not that important). The hash_function depends on the type of the
bucketing column. For an int, it's easy, {{{hash_int(i) == i}}}. For example, if user_id were
an int, and there were 10 buckets, we would expect all user_id's that end in 0 to be in bucket
1, all user_id's that end in a 1 to be in bucket 2, etc. For other datatypes, it's a little
tricky. In particular, the hash of a BIGINT is not the same as the BIGINT. And the hash of
a string or a complex datatype will be some number that's derived from the value, but not
anything humanly-recognizable. For example, if user_id were a STRING, then the user_id's in
bucket 1 would probably not end in 0. In general, though, distributing rows based on the hash
will give you a even distribution in the buckets.
+
+ So, what can go wrong? As long as you {{{set hive.enforce.bucketing = true}}}, and use the
syntax above, the tables should be populated properly. Things can go wrong if the bucketing
column type is different during the insert and on read, or if you manually cluster by a value
that's different from the table definition
+